For a single prediction, sure. But the model doesn't make a single prediction and then stop existing. It makes an unbounded number of future predictions and under that assumption it is reasonable to expect the model to behave optimally under Bayes rule - which has a unique global minimizer at E[(Y-F(X))^2].

So counter to your claim that "It doesn't trivially imply that the model would learn sensible distributions under uncertainty" I think you need to consider what it means to optimally update a posterior under the global minimizer of the true conditional distribution.