Accurate segmentation of abdominal organs from medical images is an essential part of the surgical planning and computer-aided disease diagnosis. Many existing algorithms are specialized to the segmentation of healthy organs. Cystic pancreas segmentation is especially challenging due to its low contrasted boundaries, variability in shape, location and the stage of the pancreatic cancer. We present a semi-automatic segmentation algorithm for pancreas with cysts. In contrast to the automatic segmentation approaches for healthy pancreas segmentation which are amenable to atlas/statistical shape approaches, pancreas with cysts can have even higher variability with respect to the shape of pancreas due to the size and shape of the cyst(s) and hence, can show fine results only with semi-automatic steerable approaches. We use a novel combination of random walker and region grow approaches to delineate the boundaries of pancreas and cysts with respective best dice coefficients of 85.10% and 86.65%, and respective best volumetric overlap errors of 26.04% and 23.54%. Results show that the proposed algorithm for cystic pancreas segmentation is accurate and stable.