TL; DR: Polynomial commitment schemes can provide proof of custodylike schemes that prove custody of data in a noninteractive way.
Background
In order to avoid the “honest but lazy” validator problem, validators have to compute a “proof of custody” that shows that they actually possess a copy of the data that is being signed. The current proof of custody construction mixes an ephemeral secret only known to the validator with the data and computes one bit. Later, validators have to reveal their ephemeral secret and everyone can check the custody bit was computed correctly.
This construction has two disadvantages that can be addressed using pairingbased polynomial commitment schemes:

It is interactive, requiring an onchain challenge game

It is not aggregatable, so proof size is proportional to the number of validators in an attestation
Number 2 is not a huge problem as attestations only grow one bit per validator.
One scheme that does not have both of these disadvantages can be constructed using recursive zkSNARKS. However this is currently not practical due to any such system requiring millions of constraints, so it is extremely expensive. As shown below, using the lighter machinery of polynomial commitments we can create a system that is still expensive, but can actually be computed within an epoch and could be very well doable given an elliptic curve operation ASIC that is currently in discussion (the most important component in a SNARK ASIC).
Outline
I will present two schemes. The first is based on the well known Kate commitment scheme [1]. Its main problem is that it is not aggregatable. Scheme two derives from the “balancing of polynomials” idea that several polynomial commitment schemes (and also SNARKs) use, but is not directly related to a polynomial commitment scheme and only really useful for the proof of custody. It is aggregatable.
Bot schemes do not completely prevent outsourcing, however while the cost to the secret owner is O(n) elliptic curve multiplications where n is the size of the data, the cost to an outsourcing provider would be O(n^2), which makes it very impractical to outsource.
Kate commitments
The Kate commitment scheme is a way to commit to a polynomial f(x)=\sum_{i=0}^n f_i x^i and decommitting a value y=f(r) (i.e. giving y and a witness that proves that it is the evaluation of the previously committed polynomial at r). The witness is a single group element.
Let e: G_1 \times G_2 \rightarrow G_t be a pairing of two elliptic curves G_1 and G_2 with generators G \in G_1 and H \in G_2 (I use additive notation for group operations). We need a trusted setup that provides s^i G for i=0,\ldots, n and s H where n is the degree of the polynomial to be committed to and s is a random value not known to anyone. Then a commitment to a polynomial consists of
\displaystyle
C = \sum_{i=0}^n f_i s^i G \text{ .}
To evaluate this polynomial at x=r, compute the polynomial
\displaystyle
h(x) = \frac{f(x)  f(r)}{xr} = \sum_{i=0}^n h_i x^i \text{ .}
The witness for the evaluation f(r) = y then consists of
\displaystyle
\Pi = \sum_{i=0}^n h_i s^i G
which can be checked using the pairing equation
\displaystyle
e(\Pi, (s  r) H) = e(C  y G, H) \text{ .}
The first scheme for a noninteractive proof of custody simply uses a Kate commitment: Let f(x) be a polynomial that represents the data for which the proof of custody should be computed, for example by setting f_i to the data blocks. Then the validator uses their validator secret for r. Note that neither r nor y=f(r) have to be published in order for this to work, but only r H and y G, making this scheme perfectly safe for the validator. Also both r and the complete data are needed to compute the proof of custody efficiently (however, see the section on outsourcing on how an outsourcing scheme is possible that uses n^2 instead of n elliptic curve multiplications).
This scheme is based on the wellknown Kate commitment scheme and proves that a noninteractive proof of custody is possible without the heavy machinery of fullblown pairingbased zkSNARKS. However, it is not yet aggregatable, and the pairing on the lefthand side of the pairing equation makes aggregation impossible as it has a validatorspecific term on each side of the pairing. Scheme two below is a somewhat more experimental idea that is aggregatable.
Scheme two
This is not directly a polynomial commitment scheme, but based on similar ideas. Let’s assume that our trusted setup consists of s^i G for i=0,\ldots, n and s^i H for i=0,\ldots, 2n and in addition to their public key rH validators commit to a “multiexponential public key” M_r = \sum_{i=0}^n r^i s^i H (see below on how such a commitment can be checked). Commit to the polynomial f using C as before. Then define the proof of custody as
\displaystyle
\Pi' = \sum_{i=0}^{2n}\left(\sum_{j+k=i} r^j f_k \right)s^i H
which can be checked using the pairing equation
\displaystyle
e(C, M_r) = e(G, \Pi') \text{.}
Note that all the pairing equations are linear in the validatorspecific terms, namely M_r and P. So this proof of custody is aggregatable, meaning it is constant sized (however the constant is one elliptic group element, which is quite substantial).
Commiting to the “multiexponential public key”
As a precondition for being able to use this scheme, we need a way for a validator to prove that their commitment M_r = \sum{i=0}^n r^i s^i H is correct, i.e. the coefficients are indeed the powers of their private key. It can be checked using the geometric series identity by additionally providing rsG and r^{n+1} G. Then
\displaystyle e(G  rsG, M_r) = e(G, H) \cdot e(r^{n+1}, s^{n+1} H)
because
\displaystyle (1  rs) \sum_{i=0}^n r^i s^i = 1  r^{n+1} s^{n+1} \text{ .}
By comparing coefficients in s^n it can be shown that this is sufficient to prove M_r = \sum_{i=0}^n r^i s^i H, even if the validator is allowed to cheat when providing r^{n+1} G (they have no other option to make the equation work).
Trusted setup
One obvious disadvantage compared to interactive proof of custody schemes is the trusted setup that is required. However, a nice property of both schemes is that the only required trusted setup are monomial terms s^i G and s^i H. This means that the “updateable” setup that is used for Sonic SNARKs is applicable [2].
Outsourcing
The idea of the proof of custody is to make outsourcing difficult without giving away some secret that makes the validator slashable. As we want to make the proof noninteractive, we are directly using the validator secret and not some ephemeral secret, as in the interactive schemes. Actually both of these schemes suffer from one obvious way to oursource them: A validator can give away r^j s^i G for 0\leq i, j \leq n. This allows anyone to compute the proof of custody in their behalf. However, in order to do this, n^2 elliptic group multiplications have to be performed, versus just n for a validator computing it using their own secret. Outsourcing is thus prohibitively expensive using this scheme.
Elliptic curve multiplication ASICs can probably make these schemes practital.
Performance
The dominant component of scheme one, using Kate commitments, is elliptic group operations. Computing the commitment and proof will cost 2n elliptic curve multiplications, and with each BLS12381 multiplication costing ca. 1 ms, at n=2^{16} will cost 120 s.
Scheme two involves 3n elliptic curve multiplications, however, there are also n^2 curveorder field multiplications necessary. These are much cheaper than EC multiplications but will probably dominate at this order. It is probably not practical at this point.
MPCfriendliness
I don’t know yet how to make either scheme efficiently computable if r is Shamirshared. However, if it is additively shared, there is a way to allow this in the second scheme due to it being aggregatable, by allowing a validator to commit to several “multiexponential public keys” M_{r_0}, \ldots, M_{r_m} where r = \sum_{i=0}^m r_i. Then the proof of custody would be checked against \sum_i M_{r_i} rather than M_r.
Possible application to data availability proofs
One interesting consequence of polynomial commitment schemes is that they make it very easy to prove that a polynomial is of a low degree. Indeed by limiting the number of polynomials in the trusted setup, it can be made impossible to commit to a higherdegree polynomial at all. This can potentially be used for data availability proofs [3].
[1] https://www.iacr.org/archive/asiacrypt2010/6477178/6477178.pdf
[2] https://eprint.iacr.org/2019/099.pdf
[3] https://arxiv.org/pdf/1809.09044.pdf