What you want is a bayesian estimator. It gives you a probability distribution over all possible regression values (where the mode/expected value is the equivalent of the point estimate you are used to). The smaller the distribution the higher its estimated accuracy. You basicly get the value and its expected error all in one. No problem coding this into a neural network.