Bayesian neural networks with latent variables arescalable and flexible probabilistic models: theyaccount for uncertainty in the estimation of thenetwork weights and, by making use of latent variables,can capture complex noise patterns in thedata. Using these models we show how to performand utilize a decomposition of uncertainty inaleatoric and epistemic components for decisionmaking purposes. This allows us to successfullyidentify informative points for active learning offunctions with heteroscedastic and bimodal noise.Using the decomposition we further define a novelrisk-sensitive criterion for reinforcement learningto identify policies that balance expected cost,model-bias and noise aversion.