I. Introduction
II. Uncertainty in Deep Neural Networks
A. Data Acquisition
C. Inference
D. Predictive Uncertainty Model
E. Uncertainty Classification
III. Uncertainty Estimation
A. Single Deterministic Methods
B. Bayesian Neural Networks
C. Ensemble Methods
D. Test Time Augmentation
E. Neural Network Uncertainty Quantification Approaches for Real Life Applications
IV. Uncertainty Measures and Quality
A. Evaluating Uncertainty in Classification Tasks
B. Evaluating Uncertainty in Regression Tasks
C. Evaluating Uncertainty in Segmentation Tasks
V. Calibration
A. Calibration Methods
B. Evaluating Calibration Quality
VI. Data Sets and Baselines
VII. Applications of Uncertainty Estimates
A. Uncertainty in Real-World Applications
VIII. Conclusion and Outlook