Uncertainty Quantification (UQ) is essential for enhancing the trustworthiness of Deep Learning (DL) models in high-stakes medical imaging applications. Monte Carlo Dropout (MCD) remains one of the most widely used and foundational approaches for UQ, often serving as a baseline in comparative studies. In this work, we systematically evaluate MCD in the context of DL-assisted glioma diagnosis, focusing on a less-explored yet clinically relevant multi-task setting that combines glioma subtyping and segmentation. We investigate how key parameters of MCD, namely the number of MC samples and the dropout rate, may affect the quality of uncertainty estimates. Additionally, we disentangle epistemic and aleatoric uncertainty components to gain deeper understanding of model confidence. The results demonstrate that, when appropriately tuned, MCD produces well-calibrated uncertainty estimates. The segmentation task was primarily influenced by epistemic uncertainty, whereas aleatoric uncertainty constituted the main source of uncertainty in all classification tasks.