Multimodal emotion recognition (MER) plays a crucial role in human-centric AI applications, yet existing models struggle in low-resource scenarios due to their heavy reliance on large amounts of high-quality labeled data. To address this challenge, we propose Adaptive Collaborative Labeling for Low-Resource MER (ACL-MER), a novel framework that leverages off-the-shelf multimodal large language models (MLLMs) to effectively exploit abundant unlabeled data. Specifically, ACL-MER incorporates a diverse teacher model zoo, wherein each MLLM specializes in a specific modality and is prompted to generate chain-of-thought predictions accompanied by scalar confidence scores. Rather than directly adopting these pseudo-labels, ACL-MER introduces an adaptive refinement strategy that selectively distills knowledge based on teacher confidence, iteratively guiding the lightweight student model toward robust learning under limited supervision. Extensive experiments on two benchmarks demonstrate that ACL-MER consistently outperforms strong baselines, especially in extremely low-resource settings.