We consider the classical problem of prediction with expert advice, but with an active learning twist. In this new setting, the algorithm may reorder the sequence of examples on which a prediction is made, it aims to minimize regret as usual, but it can observe only a small fraction of the true labels along the way. We consider a variant of the Hedge algorithm for this setting, and we show that under a very particular combinatorial constraint on the matrix of expert predictions we can obtain a very strong regret guarantee while querying very few labels. This constraint, which we refer to as $\theta $-compactness, can be viewed as a non-stochastic variant of the disagreement coefficient, another popular parameter used to reason about the sample complexity of active learning in the IID setting. We also give a polynomial time algorithm to calculate the $\theta $-compactness of a matrix up to an approximation factor of 3.