Within-subject multimodal groupwise registration aims to align a group of multimodal images into a common structural space. Existing groupwise registration methods often rely on intensity-based similarity measures, but can be computationally expensive for large sets of images. Some methods build statistical relationships between image intensities and anatomical structures, which may be misleading when the assumption of consistent intensity-class correspondences do not hold. Additionally, these methods can be unstable in batch group registration when the number of anatomical structures varies across different image groups. To tackle these issues, we propose GMM-CoRegNet, a weakly supervised deep learning framework for within-subject multimodal groupwise registration. A prior Gaussian Mixture Model (GMM) consolidating the image intensities and anatomical structures is constructed using the label of reference image, then we derive a novel similarity measure for groupwise registration based on GMM and iteratively optimize the GMM throughout the training process. Notably, GMM-CoRegNet can register an arbitrary number of images simultaneously to a reference image only needing the label of reference image. We compared GMM-CoRegNet with state-of-the-art groupwise registration methods on two carotid datasets and the public BrainWeb dataset, demonstrated its superior registration performance even for the registration scenario of inconsistent intensity-class mappings.