  The development of automatic tools for early glaucoma diagnosis with color
fundus photographs can significantly reduce the impact of this disease.
However, current state-of-the-art solutions are not robust to real-world
scenarios, providing over-confident predictions for out-of-distribution cases.
With this in mind, we propose a model based on the Dirichlet distribution that
allows to obtain class-wise probabilities together with an uncertainty
estimation without exposure to out-of-distribution cases. We demonstrate our
approach on the AIROGS challenge. At the start of the final test phase (8 Feb.
2022), our method had the highest average score among all submissions.
