Biomedical image registration is nowadays ubiquitously used in science and hospitals yet research developments do not always directly translate into improved robustness and accuracy for clinical workflows. Learn2Reg has been striving to close this perceived gap by providing comprehensive metrics for fair evaluation and challenging tasks to address unmet clinical needs. This year we aim to make a further leap toward engaging both a wider range of researchers and opening up our established concepts for running a successful challenge to the community. First, Learn2Reg 2024 will be co-organised with the 11th Workshop on Biomedical Image Registration (WBIR) - this being the first time that WBIR happens at MICCAI - to bring together methodological and application oriented people from research and industry across the world. Second, Learn2Reg 2024 will be a meta-challenge that hosts three new sub-challenges proposed from within the biomedical image registration community. This will include ReMIND a multimodal intra-operative registration task, LUMIR a new large-scale unsupervised whole brain alignment task and COMULIS a biomedical application with very high resolution scans. Throughout the last years the advent of deep learning has led to a shift of the research focus of MICCAI to segmentation and classification challenges, which does not fully reflect the clinical impact of image registration. Based on our previous Learn2Reg events we believe there is an inherent unsolved problem that introduces domain specific difficulties in adapting and advancing learning techniques for the ill-posed registration problem. There is also weaker connection across research groups that work on complementary registration problems or methodological concepts. Bringing WBIR to MICCAI, joining up WBIR and Learn2Reg and enabling community-driven challenges as part of our 2024 meta-challenge will be immensely beneficial for the registration and general MICCAI community.