  Deep learning appearance-based 3D gaze estimation is gaining popularity due
to its minimal hardware requirements and being free of constraint. Unreliable
and overconfident inferences, however, still limit the adoption of this gaze
estimation method. To address the unreliable and overconfident issues, we
introduce a confidence-aware model that predicts uncertainties together with
gaze angle estimations. We also introduce a novel effectiveness evaluation
method based on the causality between eye feature degradation and the rise in
inference uncertainty to assess the uncertainty estimation. Our
confidence-aware model demonstrates reliable uncertainty estimations while
providing angular estimation accuracies on par with the state-of-the-art.
Compared with the existing statistical uncertainty-angular-error evaluation
metric, the proposed effectiveness evaluation approach can more effectively
judge inferred uncertainties' performance at each prediction.
