Adaptive proton therapy (APT) is essential for the success of proton therapy by adjusting treatment plans to anatomical changes throughout the treatment. However, on-line adaptation poses significant challenges, particularly due to the limitations of cone-beam CT (CBCT), which lacks the accuracy needed for precise dose calculations. To address this, deep learning-based synthetic CT (sCT) generation from CBCT has emerged as a potential solution. Additionally, accurate segmentation of organs at risk (OARs) is crucial for treatment adaptation, yet existing approaches often treat sCT synthesis and segmentation as separate tasks, leading to potential inconsistencies. This work proposes a multi-task model that simultaneously generates sCT images and segments OARs from CBCT, ensuring anatomical coherence between both outputs. By employing shared feature learning, this approach enhances both image quality and segmentation accuracy. Furthermore, incorporating uncertainty quantification helps assess prediction reliability, addressing a key barrier to clinical adoption. Through this strategy, we aim to enhance the robustness of APT.