A brain lesion is an area of tissue that has been damaged through injury or disease. Its analysis is an essential task for medical researchers to understand diseases and find proper treatments. In this context, visualization approaches became an important tool to locate, quantify, and analyze brain lesions. Unfortunately, image uncertainty highly effects the accuracy of the visualization output. These effects are not covered well in existing approaches, leading to miss-interpretation or a lack of trust in the analysis result. In this work, we present an uncertainty-aware visualization pipeline especially designed forbrain lesions. Our method is based on an uncertainty measure for image data that forms the input of an uncertainty-aware segmentation approach. Here, medical doctors can determine the lesion in the patient’s brain and the result can be visualize dby an uncertainty-aware geometry rendering. We applied our approach to two patient datasets to review the lesions. Our results indicate increased knowledge discovery in brain lesion analysis that provides a quantification of trust in the generated results.