Panoramic dental image segmentation is essential for automating diagnostics and treatment planning, yet deep learning models often struggle with limited annotations and anatomical variability, especially under Single-Task Learning (STL) paradigm. This study evaluates the effectiveness of Multi-Task Learning (MTL) versus STL for segmenting panoramic X-rays, using one primary task (individual tooth segmentation) and two auxiliary tasks (binary teeth and quadrant segmentation). With SegFormer-B5 as the backbone, we compare two MTL setups: shared encoder with multiple heads (MTL-MH) and shared encoder with task-specific decoders (MTL-MD). All models are trained and evaluated on the DENTEX dataset under identical conditions. MTL improves performance, increasing tooth Dice from 0.775 (STL) to 0.786 (MTL), quadrant Dice from 0.937 to 0.942, and binary Dice from 0.938 to 0.94. These results confirm that MTL enhances segmentation accuracy and generalization in dental panoramic imaging.