This paper introduces Learning-to-Measure (L2M) to address the challenges of meta-Active Feature Acquisition (meta-AFA), a sequential decision-making problem. Traditional AFA methods often struggle with scalability because they are designed for a single task and fail when trained on retrospective data containing systematic missingness in features. L2M overcomes these limitations by formalizing the meta-AFA problem to allow learning acquisition policies across diverse tasks and leveraging a pre-trained sequence-modeling or autoregressive approach to provide reliable uncertainty quantification. By coupling this uncertainty quantification with a greedy policy that maximizes conditional mutual information, L2M can select the next feature to acquire in-context without requiring retraining for every new task, demonstrating superior performance, especially when labeled data is scarce or missingness is high.