Computational pathology, a developing area of primarily deep learning (DL) solutions aiming to aid pathologists at their daily tasks, has shown promising results in research settings. In recent years, uncertainty estimation has gained substantial recognition as having high potential to bring value to DL algorithms for medical applications. But it is not trivial how to incorporate it with a DL system to obtain a real positive impact. In this work we propose a framework to spatially aggregated epistemic uncertainty in order to detect false negatives produced by a segmentation algorithm of breast cancer metastases. We show a strong correlation between the false negative segmentation areas and the aggregated uncertainty values. Furthermore, the results include examples of reducing false negatives, where the uncertainty approach led to detection of some tumour metastases that had been missed.