The quality of the segmentation of organs and pathological tissues has significantly improved in recent years by using deep learning approaches, which are typically trained with fully supervised learning. For these fully supervised learning methods, an immense amount of fully labeled training data is required, which are, especially in medicine, costly to generate. To overcome this issue, weakly supervised training methods are used, because they do not need fully labeled ground truth data. For the localization of objects weakly supervised learning has already become more important. Recently, weakly supervised learning also became increasingly important in the area of segmentation of pathological tissues. However, these currently available approaches still require additional anatomical information. In this paper, we present a weakly supervised segmentation method that does not need ground truth segmentations as input or additional anatomical information. Our method consists of three classification networks in sagittal, axial, and coronal direction that decide whether a slice contains the structure to be segmented. Then, we use the class activation maps of the classification output to generate a combined segmentation. Our network was trained for the challenging task of pancreas segmentation with the publicly available TCIA pancreas dataset and we reached Dice scores for slices of up to 0.86 and an overall Dice score of up to 0.53.