This work considers estimation of mass parameters for multi-robot coordinated lifting in the context of coordinated aerial manipulation, and develops strategies for active parameter estimation for cooperative manipulation tasks through an information-theoretic framework. The active sensing problem is formulated based on application of increasing forces to the object and detection of small motions that occur when the center of pressure exits the convex hull formed by existing contacts. In order to enable identification of informative actions, we develop and employ a closed-form solution of Cauchy-Schwarz quadratic mutual information (ICS) for non-parametric filters. The evaluation considers iterative selection from a finite set of measurements and demonstrates that choosing measurements to maximize ICS significantly improves the convergence rate of the parameter estimates compared to random and cyclic selection methods. This approach is extended to consider actuator constraints and feasible lifting configurations and achieves an 80% success rate in formation of feasible lifting configurations compared to a 53% baseline performance.