  Technological and computational advances continuously drive forward the broad
field of deep learning. In recent years, the derivation of quantities
describing theuncertainty in the prediction - which naturally accompanies the
modeling process - has sparked general interest in the deep learning community.
Often neglected in the machine learning setting is the human uncertainty that
influences numerous labeling processes. As the core of this work, label
uncertainty is explicitly embedded into the training process via distributional
labels. We demonstrate the effectiveness of our approach on image
classification with a remote sensing data set that contains multiple label
votes by domain experts for each image: The incorporation of label uncertainty
helps the model to generalize better to unseen data and increases model
performance. Similar to existing calibration methods, the distributional labels
lead to better-calibrated probabilities, which in turn yield more certain and
trustworthy predictions.
