Deep ensembles model epistemic uncertainty by analyzing the behavior of individual models trained on the same data but with different random initializations. When all models agree on their predictions, it suggests a clear and well-understood relationship between the input features and the labels, indicating low uncertainty. On the other hand, if the models produce divergent predictions, it signals that they may struggle to learn a robust representation for this data. This could be due to insufficient similar training data or inadequately descriptive input features.