  We propose a network architecture capable of reliably estimating uncertainty
of regression based predictions without sacrificing accuracy. The current
state-of-the-art uncertainty algorithms either fall short of achieving
prediction accuracy comparable to the mean square error optimization or
underestimate the variance of network predictions. We propose a decoupled
network architecture that is capable of accomplishing both at the same time. We
achieve this by breaking down the learning of prediction and prediction
interval (PI) estimations into a two-stage training process. We use a custom
loss function for learning a PI range around optimized mean estimation with a
desired coverage of a proportion of the target labels within the PI range. We
compare the proposed method with current state-of-the-art uncertainty
quantification algorithms on synthetic datasets and UCI benchmarks, reducing
the error in the predictions by 23 to 34% while maintaining 95% Prediction
Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We
also examine the quality of our predictive uncertainty by evaluating on Active
Learning and demonstrating 17 to 36% error reduction on UCI benchmarks.
