Human-robot teams operate in uncertain environments and need to accomplish a wide range of tasks. A dynamic understanding of the human’s workload can enable fluid interactions between team members. A system that seeks to adapt interactions for a humanrobot team needs to quantify the distribution of workload across the different workload components. A workload assessment algorithm capable of estimating the demand placed on the human’s visual resources is required. Further, adaptive systems will benefit from measures of uncertainty, as these measures inform interaction adaptations. Two machine learning methods’ capacity to estimate visual workload for a human-robot team operating in a non-sedentary supervisory environment are analyzed. A key finding is that the uncertainty-aware method outperforms the other approach.