  Deep learning has achieved impressive performance on many tasks in recent
years. However, it has been found that it is still not enough for deep neural
networks to provide only point estimates. For high-risk tasks, we need to
assess the reliability of the model predictions. This requires us to quantify
the uncertainty of model prediction and construct prediction intervals. In this
paper, We explore the uncertainty in deep learning to construct the prediction
intervals. In general, We comprehensively consider two categories of
uncertainties: aleatory uncertainty and epistemic uncertainty. We design a
special loss function, which enables us to learn uncertainty without
uncertainty label. We only need to supervise the learning of regression task.
We learn the aleatory uncertainty implicitly from the loss function. And that
epistemic uncertainty is accounted for in ensembled form. Our method correlates
the construction of prediction intervals with the uncertainty estimation.
Impressive results on some publicly available datasets show that the
performance of our method is competitive with other state-of-the-art methods.
