- Imprecise probability books:
Statistical Reasoning with Imprecise Probabilities, by Peter Walley (1990).
Lower Previsions by Troffaes and de Cooman (2014).
Introduction to Imprecise Probabilities edited by Augstin, Coolen, de Cooman, and Troffaes (2014).
Those three books are more or less on the same subject: Imprecise probability, mostly from a generalized Bayesian perspective. Walley is the classic text on the subject, but it's also worth mentioning Isaac Levi's 1980 philosophical work The Enterprise of Knowledge. For me, personally, the first few chapters of Lower Previsions (i.e. lower expectations) was a better introduction to the area than the first few chapters of Introduction, but everyone's different. There is material in the Introduction that I found helpful while reading Lower Previsions; their initial chapters are in some respects complementary. In addition, Introduction includes introductory survey chapters on a wide variety of topics, such as imprecise stochastic processes, that aren't covered in an introductory manner anywhere else. (Introduction also includes a chapter by Shafer and Vovk that introduces their game-theoretic probability approach in a somewhat different manner than in their books.)
- Algorithmic information theory books. Maybe these don't count as "unconventional", since they are part of an established field. However, these books contain material on probability and randomness that's foundational in a way that's very different from most foundational work on probability. Among other things, this area connects to the idea of statistical testing via e.g. Martin Löf's work on randomness and other ideas covered in in these books.
An Introduction to Kolmogorov Complexity and Its Applications by Li and Vitanyi. Classic textbook. I have found the third edition very useful, but difficult to understand at times because of typos, an attempt to avoid parts of mainstream probability theory sometimes, or lack of back-references to material introduced much earlier in the book. A fourth edition just came out, though, so that might avoid some of the drawbacks.
Information and Randomness by Calude. Not as much hand-holding as Li and Vitanyi, but maybe that's better given how L & V do it sometimes.
Algorithmic Information Theory by Chaitin. Quirky but interesting perspectives.
- More Shafer and Vovk:
They have a new 2019 book on the same topic as the book mentioned in the question. Game-Theoretic Foundations for Probability and Finance contains new results, but less of the history, philosophy, and initial hand-holding of the the book mentioned in OP's question. Up to you which is a better starting point.