Accurate segmentation of pediatric brain tumors in MRI is essential for diagnosis, treatment planning, and response assessment. In this study, we investigate uncertainty-aware segmentation of multi-subregion pediatric gliomas using the BraTS-PEDs 2025 dataset. Leveraging the nnUNet v2 framework, we establish a strong baseline and conduct a series of ablation experiments to assess the impact of technical modifications in the design of the pipelines. Key contributions include the use of skull stripping (SynthStrip), atlas-based brain subregion masking (SynthSeg), and an ensemble-based cropping approach guided by whole tumor segmentation. We also evaluate synthetic channel augmentation and multi-task learning with auxiliary skull stripping, though these did not yield performance gains. A voxel-wise ensemble framework is used to identify spatial uncertainty. Results show that whole tumor segmentation and region-specific cropping significantly improve subregion Dice scores, particularly for enhancing and non-enhancing tumor regions. All code, exploratory data analysis outputs, and experiment results are made publicly available to support reproducibility and further research.