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We propose a network architecture capable of reliably estimating uncertaintyof regression based predictions without sacrificing accuracy. The currentstate-of-the-art uncertainty algorithms either fall short of achievingprediction accuracy comparable to the mean square error optimization orunderestimate the variance of network predictions. We propose a decouplednetwork architecture that is capable of accomplishing both at the same time. Weachieve this by breaking down the learning of prediction and predictioninterval (PI) estimations into a two-stage training process. We use a customloss function for learning a PI range around optimized mean estimation with adesired coverage of a proportion of the target labels within the PI range. Wecompare the proposed method with current state-of-the-art uncertaintyquantification algorithms on synthetic datasets and UCI benchmarks, reducingthe error in the predictions by 23 to 34% while maintaining 95% PredictionInterval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. Wealso examine the quality of our predictive uncertainty by evaluating on ActiveLearning and demonstrating 17 to 36% error reduction on UCI benchmarks.

Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks