From the moment we wake up in the morning to the day's ebb when we settle in to sleep, we are bound to the task of decision-making. Some of these decisions barely register in our consciousness, if at all, while others, less shy, take a more prominent place at our mind's table. Regardless of the importance or difficulty of the decision, few, if any, are made with perfect information: being such a small of part of a large system, we can only know so much. Further, the system itself sends us noisy information for us to encode and decode as best we can. How do we do this? How do we continuously and, for the most part successfully, resolve uncertainty in order to survive and even flourish? We propose to define uncertainty in decision-making as a computational process, in line with information-processing theories of neural mechanisms. To that end, we investigate the neural correlates of uncertainty processing using functional magnetic resonance imaging (fMRI) in humans within a predictive coding framework. The field has already produced considerable evidence showing that decisions are made with the aim of maximizing utility, a process involving the dopaminergic reward system. We turn our focus to the uncertainty surrounding predictions and their concomitant errors by conducting a two-part fMRI experiment on 23 subjects. In the first session, we elicited objective, cognitive (financial) uncertainty in a gambling task. In the second session, we exposed individuals to subjective, perceptual uncertainty, in the form of visual illusions. Our fMRI results, modeled by computational definitions of surprise, confidence and information, show that 1) the brain employs computational principles to resolve uncertainty; 2) certain regions are consistently implicated in processing said uncertainty, notably insular cortex regions, across modalities (cognitive and perceptual), be it of a subjective or objective nature. These findings support the notion that the brain is an active inference machine, a paradigm within which further aspects of cognition can be investigated.