Formulating effective pharmaceutical products for dermal delivery requires optimising complex mixtures of excipients under a tight experimental budget. While surrogate model-based optimisation is widely used, most approaches typically rely on predefined acquisition strategies to balance exploitation (prediction performance) and exploration (model uncertainty), which may underexplore informative regions or miss optimal designs in data-scarce conditions. This study introduces an active learning-based optimisation framework that adaptively guides formulation development by dynamically adjusting acquisition priorities. In the proposed framework, Gaussian process regression is employed to model the relationship between formulation composition and in vitro release test (IVRT)-measured drug release. The surrogate predictions are then used to generate a candidate set that spans the trade-off between high predicted release and model uncertainty. A novel adaptive allocation strategy subsequently selects the next experimental batch by assigning candidate formulations between expected improvement-ranked and hypervolume contribution-ranked lists according to their observed empirical effectiveness. This adaptive mechanism improves the use of a limited per-batch evaluation budget and maintains an effective exploration-exploitation balance. Across five benchmark functions, the framework is the strongest batch acquisition strategy, best on most cells and tied with the best alternative on the remainder, and remains competitive with sequential Bayesian optimisation despite using roughly six times fewer model updates. Its practical value is demonstrated on a real-world, inherently batch ibuprofen-loaded poloxamer 407-based formulation task evaluated by IVRT, where it identified formulations substantially exceeding the initial best, highlighting its potential for accelerating dermal drug formulation development.