Accurate segmentation of the pancreas in magnetic resonance imaging (MRI) is essential for enhancing diagnostic and therapeutic strategies in pancreatic diseases. In this study, we explore the application of the Segment Anything Model (SAM), a state-of-the-art foundation model, for pancreas segmentation in MRI scans. We present a preliminary approach that utilizes AutoSAM, a recent work designed to optimize input prompts for the SAM decoder, aiming to improve segmentation capabilities. To evaluate the performance of our method, we employ a publicly available MRI dataset, allowing for comparison with existing segmentation techniques. Preliminary results suggest that learned prompts may lead to potential improvements in pancreas segmentation, indicating the promise of foundation models in medical imaging tasks.