Depending on the problem you may use Bayesian Neural Networks where you fit a distribution over the weights they are better calibrated but also expensive. There exists some theory on lower cost ways to make the model better calibrated / uncertainty aware. One direction is using Gaussian Process approximations an other is for example PostNet. The overal topic you can search for is uncertainty quantification