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Reliable scene understanding requires not only accurate predictions but also well-calibrated confidence estimates to ensure calibrated uncertainty estimation, especially in safety-critical domains like autonomous driving. In this context, semantic segmentation of LiDAR points supports real-time 3D scene understanding, where reliable uncertainty estimates help identify potentially erroneous predictions. While most existing calibration approaches focus on modeling epistemic uncertainty, they often overlook aleatoric uncertainty arising from measurement inaccuracies, which is especially prevalent in LiDAR data and essential for real-world deployment. In this work, we introduce a sampling-free approach for estimating well-calibrated confidence values by explicitly modeling aleatoric uncertainty in semantic segmentation, achieving alignment with true classification accuracy and reducing inference time compared to sampling-based methods. Evaluated on the real-world SemanticKITTI benchmark, our approach achieves 1.70\% and 1.33\% Adaptive Calibration Error (ACE) in semantic segmentation of LiDAR data using RangeViT and SalsaNext models, and is more than one order of magnitude faster than the comparable baseline. Furthermore, reliability diagrams reveal that our method produces underconfident rather than overconfident predictions — an advantageous property in safety-critical systems.