  We propose a new optimization framework for aleatoric uncertainty estimation
in regression problems. Existing methods can quantify the error in the target
estimation, but they tend to underestimate it. To obtain the predictive
uncertainty inherent in an observation, we propose a new separable formulation
for the estimation of a signal and of its uncertainty, avoiding the effect of
overfitting. By decoupling target estimation and uncertainty estimation, we
also control the balance between signal estimation and uncertainty estimation.
We conduct three types of experiments: regression with simulation data, age
estimation, and depth estimation. We demonstrate that the proposed method
outperforms a state-of-the-art technique for signal and uncertainty estimation.
