Shared autonomy systems allow robots to either operate autonomously or request assistance from a human operator. In such settings, the human operator may exhibit sub-optimal behaviours, influenced by latent variables such as attention level or task proficiency. In this paper, we consider shared autonomy systems composed of multiple robots and one human. In this setting, we aim to synthesise a controller that selects, at each decision step, the actions to be taken by each robot and which (if any) robot the human operator should assist. To efficiently allocate the human operator to a robot at any given time, we propose a controller that reasons about the uncertainty over the latent variables impacting the human operator's performance. To ensure scalability, we use an online bidding system, where each robot plans while considering its belief over the human's performance, and bids according to the direct benefit of human assistance and how much information will be gained by the system about the human. We experiment on two domains, where we outperform approaches for allocation of human assistance that do not consider the human's latent variables, and show that the performance of the overall system increases when robots consider the information gained by requesting human assistance when bidding.