A practical way to enhance the confidence of the predictions made by Machine Learning (ML) models is to enrich them with trustworthiness addons such as Uncertainty Quantification (UQ). Existing UQ paradigms capture two intertwined components (epistemic and aleatoric), but few of them evaluate their disentanglement, even less on real data. We thus propose and implement a methodology to assess the effectiveness of uncertainty disentanglement despite the absence of ground truth in real datasets. To do so, we use a data withdrawal-based strategy to simulate Out-of-Distribution (OOD) data and evaluate four state-of-the-art UQ approaches.