Model that predicts probability of correctness of another model
This is a problem known as quality estimation (QE) in the application of Machine Translation (there's a regular Shared Task about it). The goal is to train a model to predict the quality of the output of a ML system.
There is an important theoretical obstacle to this kind of task: if such a QE model was able to predict perfectly, then in theory it would be possible to create a perfect model for the original task by trying many different models/parameters until the QE system predicts a high level of quality. What this means practically is that a QE model can only be as good as the system it's trying to estimate since it relies mostly on the same information, otherwise the system is clearly sub-optimal. Essentially the best the QE system can do is to determine how hard it is to correctly predict a particular input image, but it cannot predict with any certainty whether the actual prediction is correct or not.